Mining Generalized Term Associations: Count Propagation Algorithm

Jonghyun Kahng, Wen-Hsiang Kevin Liao, and Dennis McLeod, University of Southern California

We present here an approach and algorithm for mining generalized term associations. The problem is to find co-occurrence frequencies of terms, given a collection of documents each with relevant terms, and a taxonomy of terms. We have developed an efficient Count Propagation Algorithm (CPA) targeted for library applications such as Medline. The basis of our approach is that sets of terms (termsets) can be put into a taxonomy. By exploring this taxonomy, CPA propagates the count of termsets to their ancestors in the taxonomy, instead of separately counting individual termset. We found that CPA is more efficient than other algorithms, particularly for counting large termsets. A benchmark on data sets extracted from a Medline database showed that CPA outperforms other known algorithms by up to around 200% (half the computing time) at the cost of less than 20% of additional memory to keep the taxonomy of termsets. We have used discovered knowledge of term associations for the purpose of improving search capability of Medline.

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